Most people can tell immediately when they are talking to a machine rather than a human, even as AI agents become better at solving customer support problems.
Smallest.ai, a startup founded in late 2024, believes the next step in voice agents does not come from making large language models faster. Instead, the company is building smaller, specialised models designed for human conversation. The goal is to make speaking to an AI agent indistinguishable from talking to a person.
The company is developing a small voice model that mimics how humans process information by listening, thinking, and speaking at the same time.
“While I’m speaking to you, you’re already thinking, and you might interrupt me if I talk for too long,” Sudarshan Kamath, founder and CEO of Smallest.ai, told TechCrunch. “This is exactly how the startup’s model is designed to work.”
Smallest.ai has raised $13 million in a Series A round, led by Seligman Ventures with participation from Sierra Ventures and 3one4 Capital. The fresh capital brings the startup’s total funding to over $21 million.
Kamath explained that large language models wait for an entire prompt before starting to think. While that latency is acceptable in a text chat, even a short pause feels unnatural in a voice conversation. “If you think about how we are talking, I’m not giving you like a large clipping of my audio, and then you start thinking.”
The startup’s model acts as a real-time intelligence layer that enables natural customer conversations on specific topics with virtually zero response lag. If the model encounters a subject outside its limited knowledge base, Smallest.ai hands off the query to a large foundational model, briefly placing the customer on hold to “research” the issue — just as a real human would do.
Kamath believes that all AI agents will soon rely on two models: a small voice model for real-time interaction, and an “offline” LLM that is called upon as needed to solve complex problems.
Unlike large foundational models, Smallest.ai focuses strictly on voice-specific nuances, such as handling diverse accents, supporting dozens of languages, and operating in noisy environments.
Existing customers include companies in the voice space, including RingCentral and Truecaller. Kamath said that any customer support company, including newer ones like Sierra and Decagon, is a potential customer for the startup.
When asked why a well-funded AI customer support company would not build its own voice model, Kamath said that for customer support startups, becoming “extremely good at doing voice is a distraction from their core business.”
Smallest.ai competes with voice AI leader ElevenLabs, as well as Cartesia and regional players like Sarvam that focus on local languages.
While some competitors apply voice AI to use cases, like audio dubbing and podcasting, Smallest.ai focuses strictly on real-time conversational voice agents for its enterprise customers.
“We want our models to break the Turing test,” Kamath said. “You should speak to our model and not know it’s AI or human. That’s the sole focus of the company.”
What it means
For people making things, the change is a shift from waiting for pauses to having conversations that flow like they do between humans. The company is not trying to make the AI smarter in a general sense, but faster in a specific way that matches human timing. This allows for interruptions and immediate responses without the robotic delay that currently marks a machine interaction.
